Empirical Distribution Models for Slenderness and Aspect Ratios of Core Particles of Particulate Wood Composites
Bibliographic record
Abstract
Particle geometry was characterized for particleboard furnish prepared through hydrolysis of finished commercial particleboard procured from six Canadian plants. Particles samples were screened into seven particle size classes. Particles retained on 0.5-mm mesh were considered core particles and further partitioned into core-fine, medium, and coarse. Individual particles were then randomly selected for geometrical characterization and distribution fitting. About 80% of all screened particles by mass were between mesh sizes of 0.5 and 2 mm. There were significant differences in percentage screen masses of all particle sizes between plants. Masses of particle size greater than 1 mm of panels from two plants were significantly higher than the rest (0.05 α-level), whereas another plant had the highest mass of particle sizes retained on the 2-mm mesh. Particles retained on the 1-mm mesh showed the largest percentage mass variation among all plants. It was found that aspect ratio was a better geometrical indicator for predicting screw withdrawal resistance than any of the absolute dimensions, and increase in core-fine particles increases internal bond strength. Based on maximum likelihood and Akaike's Information Criterion, a log normal distribution was the best fit for all geometrical descriptors of most particle types; gamma and two-parameter Weibull were better fits for length and aspect ratio for most medium particles with gamma being the better of the two.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".